In recent years, machine learning data-driven based models have emerged and proven to significantly reduce the processing time for weather forecasting [1]}. Unlike model-driven NWP models, machine learning models make no assumptions about underlying physical factors influencing the weather [1]}. They rather rely on large amounts of historical weather observations which are used to extract and learn the relationship between input and output data which then are used to predict future weather conditions [3]}, [4]}, [5]}.
